32 Mapping the co-benefits of reducing low-value care and the environmental impacts of care: a literature analysis & research agenda
Bibliographic record
Abstract
Objectives Reducing low-value care and improving healthcare’s climate readiness are critical factors to improve the sustainability and resilience of health systems across the globe. By definition, low-value care generates carbon emissions, waste and pollution without improving patient or population health. Thirty percent of clinical care has been deemed low- or no value and as much as 80% of healthcare carbon emissions arise from clinical care. Little is known about the knowledge, research, interventions and practice change being developed on the co-benefits of reducing low-value care and reducing the environmental impacts of care. The objective of this study was to advance the field by developing foundational knowledge, through a literature analysis (scoping review & bibliometric analysis) and research agenda synthesis, of key aspects of co-benefits research and practice change. Methods We identified, collected and synthesized data from research and practice change publications on reducing low-value care and improving the climate resilience and sustainability of health systems. Four databases, Medline, Embase, Scopus and CINAHL, were searched from inception to January 2023. We followed scoping review methodology to collect and analyze the data. The database searches identified 1794 unique articles for title and abstract screening; 264 articles moved to full-text review. For the bibliometric analysis of the included articles, we analyzed authors, organizations topics, collaborations, citations and journals. Biblioshiny, additional R-based applications, and Microsoft Excel were used for publication, co-authorship and co-word analyses. Results Seventy six articles published 2013-2022 met inclusion criteria, with over 75% of the articles published since 2020. Thirty percent of the articles were empirical studies with the remainder being commentary, editorials or opinion. A quarter of the articles focused equally on the importance of reducing low-value care and improving environmental impact of healthcare; 60% of articles focused on reducing the environmental impact of care; 15% focused on reducing low-value care. The majority of articles focused on healthcare generally (32%), with the remainder focused on practices such as laboratory testing (17%), and surgery and anesthesia (15%). The majority of articles were written by multi-national teams, with first authors predominantly from Australia (42%), UK (23%) and USA (20%). The bibliometric analysis revealed distinct and geographically specific collaborations, in addition to a number of nation-spanning research groups. Reported research and practice priorities included a need for increased resource stewardship, standards, metrics and provider education. The lack of evidence, data, leadership and cohesive strategy were reported as challenges in the field. Directions for future research and practice included increasing transparency on environmental impacts and patient education and communication. Conclusions This work provides foundational knowledge to advance understanding on the co-benefits of reducing low-value care and improving environmental sustainability. This literature synthesis mobilizes existing knowledge on co-benefits research and practice to support the development of solution to address low-value care and the climate resilience and sustainability of health systems. Next steps include consensus meeting to develop a shared research agenda and community of practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.111 | 0.124 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".